Control of Mobile Robots is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts - to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge we have set out to address through the different pieces in the course.

Taught By

Dr. Magnus Egerstedt

Professor

Transcript

So after all this work that we've gone through, we've seen how to design control systems, we've seen how to take different controllers and put them together in a hybrid navigation architecture. We've seen how to deal with practical issues, like the fact that obstacles are indeed, not points, and that we're never going to be able to measure exactly where we are, so we need to factify the guards. we have tweaked our parameters. We're actually ready to do it for real. So the presentation, or slide part of this lecture is very short. It's this. Enough talk. Let's do it. So now, we are indeed ready to, see all our hard work pay off by deploying these, rather elegant algorithms that we've developed on the actual robots. And as always, I'm here with, J. P. DeLacroix/g, and we are going to, take our old friend, the computer, differential drive mobile robot, through a series of. Obstacle courses where the obstacles become become more and more complicated and as you can see, the first set up is a convex/g obstacle. We saw in the lecture that in theory, the robot can actually get stuck in one of these local minimum where the goal to goal and obstacle avoidance behaviors will cancel each other out. In practice, for an obstacle of this size, that ain't going to happen. Because the world is indeed a noisy place and. The noise will always kick the robot out of these particular local minima. So, what we're going to see first is goal-to-goal and avoid obstacles and together that should be enough to have the robot navigate this single convex obstacle. So as we can see the robot starts in a go-to-goal behavior. It approaches the obstacle, and there, the infrared sensors picks up the obstacle, and the robot successfully navigates around it. Now. It's safe to go back to go-to-goal, and as we can see, the robot successfully managed to navigate this particularly simple environment with only two behaviors. So now, let's take our two favorite behaviors, go-to-goal and avoid obstacle, and set them up to fail. As we saw in the lectures, there's really no way for these two behaviors to successfully negotiate a. Non-convex, obstacle. In this case we have a cul-de-sac that we will try to get around using onlt these two behaviors, but we are not, to be completely honet, honest, expecting this to go all that well. So, the robot starts out in a go to-go mode and it is entering the cul-de-sac, and it's going to start avoiding the obstacles here, but it doesn't quite know where to go, because. It's pulled away, but the goal to goal is pulling it forward and the poor robot is completely confused and really has no of punching its way out of this cul da sac with only two behaviors. And as we can see the end result is somewhat disastrous. So that was rather depressing. The poor robot was stuck in the cul de sac without really anywhere to go and obstacle avoidance and going to go and kept pulling it in different directions and it ended with a mild disaster. We have now seen in the lectures that the way around this problem is really to introduce a third behavior, induced mode Follow-wall, which will allow the robot to purposefully make its way out of the culdesac and once it has a clear shot to the goal, and sufficient for forward progress has been made follow-wall will be released and go-to-goal will again take over. So JP, let's see the grand finale here, with the full blown hybrid navigation architecture in. Action. The robot starts out, again, in a go-to-goal mode, and enters the culdesac, and what's going to be different now, is the obstacle avoidance is going to be replaced by a follow-wall, and the robot purposefully makes its way out of the culdesac along one of the obstacle arms. And, as you can see. The problems we had before with the confusion. And being pulled back and forth between the 2 different behaviors, is no longer there. now the robot has successfully navigated around the obstruction. And is back into the goal to goal behavior.

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